Methods and Systems for Clustering of Repair Orders Based on Inferences Gathered from Repair Orders

ABSTRACT

A processor may determine that a particular computer-readable vehicle repair order (RO) (e.g., including first and second RO portions) corresponds to an existing cluster of ROs due to the particular RO including RO data that refers to a particular vehicle symptom. The processor may determine that the first RO portion includes first data representative of a non-specific vehicle component and may then responsively also determine that the second RO portion includes second data that the at least one processor can use to determine a specific vehicle component associated with the particular RO. Responsively, the processor may determine the specific vehicle component based on the first and second data and may then add the particular RO to a different cluster of ROs that is arranged to contain ROs that correspond to the particular vehicle symptom and to the specific vehicle component.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation application of U.S. patentapplication Ser. No. 16/160,100.

U.S. patent application Ser. No. 16/160,100 was filed on Oct. 15, 2018and is entitled “Methods and systems for clustering of repair ordersbased on inferences gathered from repair orders.” U.S. patentapplication Ser. No. 16/160,100 published on Feb. 14, 2019 as UnitedStates Patent Application Publication No. 2019/0050826 A1. U.S. patentapplication Ser. No. 16/160,100 is a continuation application of U.S.patent application Ser. No. 14/933,337.

U.S. patent application Ser. No. 14/933,337 was filed on Nov. 5, 2015and is entitled “Methods and systems for clustering of repair ordersbased on inferences gathered from repair orders.” U.S. patentapplication Ser. No. 14/933,337 published on May 11, 2017 as UnitedStates Patent Application Publication No. 2017/0132577 A1 and issued onNov. 20, 2018 as U.S. Pat. No. 10,134,013.

U.S. patent application Ser. No. 16/160,100, U.S. patent applicationSer. No. 14/933,337, United States Patent Application Publication No.2019/0050826 A1, and United States Patent Application Publication No.2017/0132577 A1 are incorporated herein by reference.

INCORPORATION BY REFERENCE

U.S. patent application Ser. No. 14/270,994, filed on May 6, 2014, isincorporated herein by reference, as if fully set forth in thisdescription.

BACKGROUND

Many products produced by manufacturers occasionally have to berepaired. Many owners are unequipped or otherwise unable to repaircertain products. Such owners may depend on professional repairtechnicians to service or repair the owner's product.

The repair technicians typically repair products at a product repairshop. A repair shop has traditionally produced a repair order (RO) tocapture a variety of information regarding a request for servicing orrepairing a product. As an example, the captured information can includeinformation identifying the product, the product's owner, the repairshop, the date of repair, and the type of repair or service needed orperformed. The RO can exist in various formats such as a paper format oran electronic format.

Product manufacturers use a significant amount of resources (e.g., humanand financial) to generate repair information, such as repair manualsand technical service bulletins, that repair technicians can referencewhile diagnosing and repairing a product. It may be beneficial toproduct manufacturers if the repair information can be generatedautomatically by a computing device. It may be beneficial to productmanufacturers and repair technicians if the repair information providedto the repair technicians is automatically generated based on ROinformation.

OVERVIEW

Example implementations are described herein. In one aspect, a method isdisclosed. The method involves determining, by at least one processor,that a particular computer-readable vehicle repair order (RO)corresponds to an existing cluster of ROs due to the particular ROincluding RO data that refers to a particular vehicle symptom. Thisexisting cluster is arranged to contain ROs that correspond to theparticular vehicle symptom. Also, the particular RO includes at least afirst RO portion and a second RO portion. The method also involvesdetermining, by the at least one processor, that the first RO portionincludes first data representative of a non-specific vehicle component.The method additionally involves, in response to determining that thefirst RO portion includes the first data, the at least one processordetermining that the second RO portion includes second data that the atleast one processor can use to determine a specific vehicle componentassociated with the particular RO. The method further involves, inresponse to determining that the second RO portion includes the seconddata, the at least one processor determining the specific vehiclecomponent based on the first and second data. The method yet furtherinvolves, in response to determining the specific vehicle component, theat least one processor adding the particular RO to a different clusterof ROs. This different cluster is arranged to contain ROs thatcorrespond to the particular vehicle symptom and to the specific vehiclecomponent.

In another aspect, a computing system is disclosed. The computing systemincludes a data storage device having stored thereon a plurality ofcomputer-readable vehicle repair orders (ROs). The computing system alsoincludes at least one processor coupled to the data storage device andprogrammed to determine that a particular RO of the plurality of ROscorresponds to an existing cluster of ROs due to the particular ROincluding RO data that refers to a particular vehicle symptom. Thisexisting cluster is arranged to contain ROs that correspond to theparticular vehicle symptom. Also, the particular RO includes at least afirst RO portion and a second RO portion. The at least one processor isalso programmed to determine that the first RO portion includes firstdata representative of a non-specific vehicle component. The at leastone processor is additionally programmed to, in response to determiningthat the first RO portion includes the first data, determine that thesecond RO portion includes second data that the at least one processorcan use to determine a specific vehicle component associated with theparticular RO. The at least one processor is further programmed to, inresponse to determining that the second RO portion includes the seconddata, determine the specific vehicle component based on the first andsecond data. The at least one processor is yet further programmed to, inresponse to determining the specific vehicle component, add theparticular RO to a different cluster of ROs. This different cluster isarranged to contain ROs that correspond to the particular vehiclesymptom and to the specific vehicle component.

In yet another aspect, a computer readable medium is disclosed. Thecomputer readable medium has stored thereon instructions executable byat least one processor to cause a computing system to performoperations. The operations involve determining that a particularcomputer-readable vehicle repair order (RO) corresponds to an existingcluster of ROs due to the particular RO including RO data that refers toa particular vehicle symptom. This existing cluster is arranged tocontain ROs that correspond to the particular vehicle symptom. Also, theparticular RO includes at least a first RO portion and a second ROportion. The operations also involve determining that the first ROportion includes first data representative of a non-specific vehiclecomponent. The operations additionally involve, in response todetermining that the first RO portion includes the first data,determining that the second RO portion includes second data that the atleast one processor can use to determine a specific vehicle componentassociated with the particular RO. The operations further involve, inresponse to determining that the second RO portion includes the seconddata, determining the specific vehicle component based on the first andsecond data. The operations yet further involve, in response todetermining the specific vehicle component, adding the particular RO toa different cluster of ROs. This different cluster is arranged tocontain ROs that correspond to the particular vehicle symptom and to thespecific vehicle component.

These as well as other aspects and advantages will become apparent tothose of ordinary skill in the art by reading the following detaileddescription, with reference where appropriate to the accompanyingdrawings. Further, it should be understood that the embodimentsdescribed in this overview and elsewhere are intended to be examplesonly and do not necessarily limit the scope of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

Example embodiments are described herein with reference to the followingdrawings.

FIG. 1 is a block diagram of a system in accordance with one or moreexample embodiments.

FIG. 2 is a block diagram of a vehicle repair data (VRD) system inaccordance with one or more example embodiments.

FIG. 3 is a block diagram showing a vehicle repair tool in accordancewith one or more example embodiments.

FIG. 4 shows a repair order in accordance with one or more exampleembodiments.

FIG. 5 shows a repair order including auto-generated repair-hint andmeta-data.

FIG. 6 is a flowchart depicting a set of functions that can be carriedout in accordance with one or more example embodiments.

FIGS. 7A to 7B illustrate example movement of an RO between clusters inaccordance with one or more examples embodiments.

FIGS. 8A to 8B illustrate example addition of a new RO to a cluster inaccordance with one or more examples embodiments.

DETAILED DESCRIPTION I. Introduction

This description describes several example embodiments including exampleembodiments regarding disambiguation. At least some of the exampleembodiments include, but are not limited to include, one or more of thefollowing features: determining that a particular computer-readablevehicle repair order (RO) (e.g., including at least first and second ROportions) corresponds to an existing cluster of ROs (e.g., arranged tocontain ROs that correspond to the particular vehicle symptom) due tothe particular RO including RO data that refers to a particular vehiclesymptom, determining that the first RO portion includes first datarepresentative of a non-specific vehicle component, determining that thesecond RO portion includes second data that the at least one processorcan use to determine a specific vehicle component associated with theparticular RO, determining the specific vehicle component based on thefirst and second data, and adding the particular RO to a differentcluster of ROs (e.g., arranged to contain ROs that correspond to theparticular vehicle symptom and to the specific vehicle component).

A vehicle repair tool can include any of a variety of repair tools arepair technician, a product owner, a person working at a repair shop,or some other person can use to repair a vehicle. Repairing a vehiclecan include, but is not limited to include, diagnosing a vehicle,servicing a vehicle, performing maintenance (e.g., preventivemaintenance) on a vehicle, or verifying a repair performed on a vehicleto correct a vehicle malfunction. Accordingly, a vehicle repair tool canbe referred to as one or more of the following terms: a vehiclediagnostic tool, a vehicle service tool, a vehicle maintenance tool, anda vehicle repair verification tool, or more generally, a machine.

A vehicle is a mobile machine that may be used to transport a person,people, or cargo. As an example, any vehicle described herein may bedriven or otherwise guided along a path (e.g., a paved road orotherwise) on land, in water, or in the air or outer space. As anotherexample, any vehicle described herein may be wheeled, tracked, railed orskied. As yet another example, any vehicle described herein may includean automobile, a motorcycle, a light-duty truck, a medium-duty truck, aheavy-duty truck, a semi-tractor, or a farm machine. As still yetanother example, any vehicle described herein may include or use anyappropriate voltage or current source, such as a battery, an alternator,a fuel cell, and the like, providing any appropriate current or voltage,such as about 12 volts, about 42 volts, and the like. As still yetanother example, any of the vehicles described herein may include or useany desired system or engine. Those systems or engines may include itemsthat use fossil fuels, such as gasoline, natural gas, propane, and thelike, electricity, such as that generated by a battery, magneto, fuelcell, solar cell and the like, wind and hybrids or combinations thereof.As still yet another example, any vehicle described herein may includean electronic control unit (ECU), a data link connector (DLC), and avehicle communication link that connects the DLC to the ECU.

Although many of the example embodiments are described with respect to avehicle, the example embodiments can be applicable to products orrepairable items other than a vehicle. As an example, the other productsor repairable items can include home appliances, such as a refrigerator,a dishwasher, or a washing machine, or a consumer electronic device,such as a television, a cellular phone, or a tablet device. Otherexamples of the other products or repairable items are also possible.Accordingly, for embodiments based on these other products or repairableitems, the term vehicle in the described embodiments can be replacedwith a name of the other product or repairable item.

In this description, the articles “a” or “an” are used to introduceelements of the example embodiments. Any reference to “a” or “an” refersto “at least one,” and any reference to “the” refers to “the at leastone,” unless otherwise specified, or unless the context clearly dictatesotherwise. The intent of using those articles is that there is one ormore of the elements. The intent of using the conjunction “or” within adescribed list of at least two terms is to indicate any of the listedterms or any combination of the listed terms. The use of ordinal numberssuch as “first,” “second,” “third” and so on is to distinguishrespective elements rather than to denote a particular order of thoseelements. For purpose of this description, the terms “multiple” and “aplurality of” refer to “two or more” or “more than one.”

The block diagram(s) and flow chart(s) shown in the figures are providedmerely as examples and are not intended to be limiting. Many of theelements illustrated in the figures or described herein are functionalelements that can be implemented as discrete or distributed elements orin conjunction with other elements, and in any suitable combination andlocation. Those skilled in the art will appreciate that otherarrangements and elements (e.g., machines, interfaces, functions,orders, or groupings of functions) can be used instead. Furthermore,various functions described as being performed by one or more elementsor a machine described herein can be carried out by a processorexecuting computer-readable program instructions or by any combinationof hardware, firmware, or software.

II. Example Architecture

FIG. 1 is a block diagram of a system 100 in accordance with one or moreexample embodiments. Various combinations of the elements shown in FIG.1 can be arranged as other systems or as a sub-system to carry outexample embodiments described herein. System 100 includes a vehiclerepair data (VRD) system 102 and a network 104. Network 104 can includea wide area network (WAN), such as the Internet or a portion thereof.Additionally or alternatively, network 104 can include a wirelessnetwork, a wired network, a local area network (LAN), or some other typeof network. Network 104 can include two or more of the aforementionedexample networks.

System 100 includes a vehicle repair tool (VRT) 106, and VRT 108, 110,112, 114, 116, 118, and 120. Each VRT or a combination of multiple VRTcan include or be arranged as a machine. Any VRT described herein canbe, but is not required to be, configured to generate or transmit anoriginal repair order (RO) to VRD system 102. An RO generated by a VRTcan be provided to an operator of VRD system 102 by a courier 122, suchas the United States Postal Service or the Federal Express Corporation.The operator of VRD system 102 can enter an original RO into VRD system102 using an RO manual entry device, such as an RO manual entry device202 shown in FIG. 2 . The manually-entered RO can be stored in a datastorage device, such as a data storage device 210 shown in FIG. 2 .

VRT 114, 116, 118, and 120 represent vehicle repair tools that areconfigured to perform at least one of the following functions: request arepair-hint (e.g., an auto-generated repair hint) stored at VRD system102, receive a repair-hint transmitted from VRD system 102 using network104 or otherwise provided or generated by VRD system 102, and present arepair-hint by a user interface. A repair-hint generated by VRD system102 can be provided to an operator of a VRT, such as VRT 114, by courier122. As an example, courier 122 can provide the repair-hint by providingthe VRT operator with a computer-readable medium, such as a CD-ROM,including a repair-hint generated by VRD system 102. VRT 116, 118, and120 can receive a repair-hint generated by VRD system 102 andtransmitted to the VRT using wireless or wired communications andnetwork 104.

A VRT can include a code reader, such as a one-dimensional bar codereader or a two-dimensional bar coder reader. The code reader can readand decode a code on a vehicle, such as a VIN bar code, a code on areplacement part, such as a bar code or quick-response code on packagingof a replacement part, or some other type of code. Data encoded from acode can be entered onto an original RO, such as original RO 400 shownin FIG. 4 .

Next, FIG. 2 is a block diagram showing details of a vehicle repair data(VRD) system 200. VRD system 102, shown in FIG. 1 , can be configuredsimilar to VRD system 200. VRD system 200 can be configured like VRDsystem 102 shown in FIG. 1 . VRD system 200 can include or be arrangedas a machine. VRD system 200 or one or more components thereof can bearranged or referred to as a computing system or a computer system. VRDsystem 200 can comprise, be configured as, or be referred to as a serversystem, a server device, or more simply, a server. In accordance withembodiments in which VRD system 200 operates as a server, VRD system 200can serve one or more vehicle repair tools (VRT) operating as a clientdevice to the server.

VRD system 200 includes the RO manual entry device 202, a processor 204,a user interface 206, a network interface 208, and a data storage device210, all of which can be linked together via a system bus, network, orother connection mechanism 212.

RO manual entry device 202 can include one or more devices for inputtingdata shown on a printed RO into VRD system 200 for storage as anoriginal RO within repair orders (RO) 214. As an example, RO manualentry device 202 can include a scanner device with or without an opticalcharacter recognition software application. As another example, ROmanual entry device 202 can include a keyboard for keying in (e.g.,typing) the data shown on the printed RO and sending the keyed in (e.g.,typed or otherwise entered) data to processor 204 for storage as anoriginal RO within RO 214. As yet another example, RO manual entrydevice 202 can include a device that accepts data storage devices, suchas a CD-ROM including data representing an original RO generated by aVRT. As yet another example, RO manual entry device 202 can include alaptop or desktop computing device with or connected to a display.

An original RO can be displayed by RO manual entry device 202 or userinterface 206. For any of a variety of reasons, such as security ofinformation located on an original RO, VRD system 102 can be configuredsuch that an original RO generated by a first VRT, such as VRT 106, isnot provided to a second VRT, such as VRT 116. VRD system 102 cangenerate a presentable RO based, at least in part, on information on theoriginal RO generated by the VRT 106, and provide the presentable RO toVRT 116.

A processor, such as processor 204, can include one or more generalpurpose processors (e.g., INTEL single core microprocessors or INTELmulticore microprocessors) or one or more special purpose processors(e.g., digital signal processors). A processor, such as processor 204,can be configured to execute computer-readable program instructions,such as computer-readable program instructions (CRPI) 218. For purposesof this description, processor 204 executing CRPI 218 to perform somefunction described herein can include executing a portion of CRPI 218 orthe entirety of CRPI 218. Executing a portion or the entirety of CRPI218 can include executing some of the computer-readable programinstructions multiple times. Processor 204 can be programmed to performany one or any combination of functions performed by execution of aprogram instruction of CRPI 218.

User interface 206 can include an interface to components operable toenter data or information into VRD system 200 or to components that canpresent data or information output by VRD system 200. Those componentscan be referred to as user interface components. User interface 206 caninclude one or more audio/visual ports or communication ports thatconnect to a user interface component by a wired or wireless userinterface communication link.

User interface 206 can include one or more of the user interfacecomponents. As an example, the user interface components can include aninfrared remote control device, a display device, a loud speakerconfigured to convert electrical signals to audible sounds, a keyboard,a touch screen, a pointing device, such as a computer mouse, or someother component for generating signals to enter data or information intoVRD system 200 or to present data or information output by userinterface 206.

User interface 206 can include a transmitter or transceiver to providethe data or information to another user interface component or toanother element of VRD system 200. The data or information provided byuser interface 206 can include, but is not limited to include, arepair-hint of repair-hints 220.

Network interface 208 can include an interface to one or morecommunication networks, such as network 104. For use with wirelesscommunication networks, network interface 208 can include one or moreantennas for transmitting or receiving wireless communications. Networkinterface 208 can include one or more communication ports configured toconnect to a wired communication link of a network, such as a coaxialcable, an Ethernet cable, a fiber optic cable, a digital subscriber line(DSL), a telephone line of a public switched telephone network (PSTN) orsome other wired connector. Network interface 208 can include a networkcontroller including a transmitter, a receiver, or a transceiver. Thetransmitter or transceiver can provide data or information to acommunication port for transmission as network communications over theconnected network. The receiver or transceiver can receive data orinformation received at a communication port from the connected network.

A data storage device, such as such as data storage device 210 or anyother data storage device discussed in this description or includedwithin a device or system described in this description, may include anon-transitory computer-readable medium, a transitory computer-readablemedium, or both a non-transitory computer-readable medium and atransitory computer-readable medium. In one respect, a non-transitorycomputer-readable medium may be integrated in whole or in part with aprocessor. In another respect, a non-transitory computer-readablemedium, or a portion thereof, may be separate and distinct from aprocessor.

A non-transitory computer-readable medium may include, for example, avolatile or non-volatile storage component, such as an optical,magnetic, organic or other memory or disc storage. Additionally oralternatively, a non-transitory computer-readable medium may include,for example, a random-access memory (RAM), a read-only memory (ROM), aprogrammable read-only memory (PROM), an erasable programmable read-onlymemory (EPROM), an electrically erasable programmable read-only memory(EEPROM), a compact disk read-only memory (CD-ROM), or another memorydevice that is configured to provide data or CRPI to a processor.

A transitory computer-readable medium may include, for example, CRPIprovided over a communication link, such as a communication link whichis connected to or is part of the network 104. The communication linkmay include a digital or analog communication link. The communicationlink may include a wired communication link or a wireless communicationlink.

A computer-readable medium may be referred to by other terms such as a“computer-readable storage medium,” a “data storage device,” a “memorydevice,” a “memory,” or a “computer-readable database.” Any of thosealternative terms may be preceded with the prefix “transitory” or“non-transitory.”

Data storage device 210 can store a variety of data. The data stored bydata storage device 210 can be data that was provided to data storagedevice 210 for storage from RO manual entry device 202, processor 204,user interface 206 or network interface 208. As shown in FIG. 2 , datastorage device 210 can store repair orders (RO) 214, a taxonomy termdatabase 216, computer-readable program instructions (CRPI) 218, repairhints 220, meta-data 222, vehicle leverage data 224, parts leverage data226, text strings 228, and search terms 230. Search terms 230 caninclude, but is not limited to, vehicle-identification (i.e.,vehicle-ID) search terms 232, such as year/make/model/engine (Y/M/M/E)attributes, and symptom criterion 234.

RO 214 can include computer-readable RO. The computer-readable RO can bearranged as a structured query language (SQL) file, an extensible markuplanguage (XML) file, or some other type of computer-readable file ordata structure. The RO within RO 214 can be received from RO manualentry device 202, from network interface 208 by way of network 104, orfrom another device. The RO within RO 214 can be an original RO, such asRO generated by a VRT shown in FIG. 1 or entered using RO manual entrydevice 202, or a presentable RO generated by VRD system 200.

FIG. 4 shows an example original RO 400. Original RO 400 can begenerated by a VRT, such as any VRT shown in FIG. 1 . Original RO 400can include a computer-readable-data RO (or more simply,computer-readable RO) transmitted over network 104. Original RO 400 caninclude a paper-copy RO, such as carbonless multi-sheet RO or some othertype of paper-copy RO. Original RO 400 can include both acomputer-readable-data version and a paper-copy version. A paper-copy ROcan be generated without using a VRT. A computer-readable RO generatedfrom a paper-copy RO can be an original RO.

Original RO 400 includes a service provider identifier 402, a date ofservice identifier 404, a customer indicator 406 that indicates acustomer seeking service of a given vehicle, vehicle information 408that indicates the given vehicle, vehicle service requests 410, 412, and414 indicating the complaint(s) or service(s) requested by the customer,parts information 416 indicating parts obtained for servicing the givenvehicle, service procedure information 418, 420, and 422 carried out onthe given vehicle, and a vehicle-usage indicator 430 (e.g., vehiclemileage data that indicates a number of miles the given vehicle has beendriven). The vehicle-usage indicator 430 on original RO 400 can indicatea driven distance using kilometers or some other units as an alternativeor in addition to vehicle mileage data. In addition to or as analternative to indicating a distance, the vehicle-usage indicator 430can include a time-used indicator such as an hours indicator indicating,for example, how long a vehicle or an engine has been used.

Service provider identifier 402 can include information that indicates aname and geographic location of the service provider. Vehicleinformation 408 can include a vehicle identification number (VIN) 432associated with the given vehicle and a description of the givenvehicle. Service procedure information 418, 420, and 422 can includeinformation within distinct RO sections 424, 426, and 428, respectively,of original RO 400. The service procedure information within any onedistinct RO section 424, 426, and 428 can be unrelated to the serviceprocedure information with any other distinct section. Alternatively,two or more distinct sections including service procedure informationcan pertain to related service operations performed on the givenvehicle.

Original RO 400 includes labor operation codes (LOCs). The LOCs canconform to those defined by a vehicle manufacturer, a service providerthat generates an RO, a service information provider, such as MitchellRepair Information, LLC, Poway, Calif., or some other entity. Forsimplicity of FIG. 4 , the LOCs are shown within parenthesis, such as(C45) and (C117, C245). Distinct LOC within parenthesis are separate bya comma. Each labor operation code (LOC) can refer to a particularoperation performed to the given vehicle. Processor 204, executing CRPI218, can use a LOC to determine what type of service or repair operationwas performed to the given vehicle. Using the LOC in that manner ishelpful if other information regarding that operation is incomplete ordescribed using non-standard phrases or terms. Processor 204 can alsouse LOC to determine context for the service procedure information on orwithin the RO.

Multiple portions of text on an RO, such as original RO 400, can begrouped as phrases. When comparing contents of an RO to various terms oftaxonomy term database 216, such as mapping terms, standard terms, orcontext terms, words within a given proximity to one or more other wordson original RO 400 can be grouped as a phrase to be compared to themapping, standard, or context terms. The given proximity can be within Xwords, where X equals 1, 2, 3, 4, 5, or some other number of words. Asan example, service procedure information 418 states “Checkstarter/ignition system.” The words “Check” and “ignition system” arewithin 3 words of one another. In accordance with an embodiment in whichthe given proximity is 4 word, the words “Check” and “ignition system”can be grouped as the phrase “Check ignition system” for comparison tomapping, standard, context terms, or labor operation codes.

The mapping, standard, context terms, or labor operation codes can bestored as part of taxonomy term database 216. Taxonomy term database 216can include data that identifies words or phrases that are associatedwith one another. The association can be based on the words or phraseshaving a common meaning. The words or phrases identified as beingassociated with one another can be referred to a “taxonomy databasegroup” or, more simply, a “taxonomy group.”

Taxonomy term database 216 can include one or more taxonomy groups, andeach taxonomy group can include one or more taxonomy terms (e.g., wordsor phrases). As an example, taxonomy term database 216 can include datathat identifies the following phrases as a taxonomy group: (i) stallswhen cold, (i) engine quits when temperature is low, (iii) engine diesin the morning, (iv) dies in the morning, (v) dies in the AM, and (vi)engine stalls on cold mornings.

Each taxonomy group can be associated with a standard term, which couldbe a first word or first phrase added to the taxonomy group.Alternatively, a word or phrase subsequently added to the taxonomy groupcan be the standard term for the taxonomy group. The words or phrasesother than the standard term within a taxonomy group can be mappingterms. The words or phrases within each taxonomy group can be obtainedfrom an RO. An administrator can approve adding or modifying anytaxonomy group by, for example, processor 204 executing CRPI 218. Termswithin taxonomy term database 216 can be compared to terms on acomputer-readable RO. A mapping term on an original RO and found withina given taxonomy group can be represented on a presentable RO by astandard term for the given taxonomy group.

RO 214 can include original RO 400 as a computer-readable version oforiginal RO 400. RO 214 can include one or more other computer-readableRO arranged like original RO 400 and one or more other computer-readableRO arranged in an RO configuration that differs from original RO 400.The other RO configurations typically include at least one of the typesof information described above as being a part of original RO 400.

An RO stored within RO 214, such as original RO 400 or another RO, caninclude searchable text or symbols (e.g., text, symbols, or text andsymbols). As an example, a symbol on an RO can include an empty checkbox or a checkbox and a checkmark inside the checkbox. Original RO 400can be modified to include a presentable RO 500 (shown in FIG. 5 ) thatrepresents original RO 400 or data thereon. Additionally oralternatively, presentable RO 500 can be distinct and separate fromoriginal RO 400.

Processor 204 can search the text, symbols or other content on an RO ofRO 214 or the meta-data associated with an RO to associate an RO withina computer-readable cluster of RO (or more simply, an RO cluster). Eachcluster of RO can be associated with defined RO attributes, such as adiagnostic trouble code (DTC), action, or component listed on the RO.Other attributes of the information recorded on an RO can be associatedwith an RO cluster. Table 1 shows data identifying twenty-five clustersidentified with ID 1 through 25, inclusive. The cluster size indicateshow many RO have been associated with the respective cluster. Thecluster size can be modified as or after additional RO are added to RO214 or after an RO is transferred from one cluster to a differentcluster. Table 1 shows examples of DTC, Action, and component attributesassociated with each respective RO cluster.

TABLE 1 Cluster Cluster ID Size DTC Action Component(s) 1 3,101 P0303Replaced Ignition Coil 2 3,086 P0303 Replaced Spark Plug 3 2,982 P0302Replaced Ignition Coil 4 2,957 P0304 Replaced Spark Plug 5 2,831 P0171Replaced Oxygen Sensor 6 2,813 P0325 Replaced Knock Sensor 7 2,762 P0301Replaced Spark Plug 8 2,713 P0320 Replaced Crankshaft Position Sensor 92,624 P0404 Replaced Exhaust Gas Recirculation Valve 10 2,609 P0302Replaced Spark Plug 11 2,603 P0303 Replaced Spark Plug Wire, Spark Plug12 2,328 P0161 Replaced Oxygen Sensor 13 2,324 C1500 Replaced FuelFilter, Fuel Tank Module 14 2,232 P0301 Replaced Spark Plug Wire, SparkPlug 15 2,225 P0302 Replaced Spark Plug Wire, Spark Plug 16 2,107 P0300Replaced Ignition Coil 17 2,104 P0305 Replaced Ignition Coil 18 2,088P0171, Replaced Mass Airflow Sensor P0174 19 2,007 P0134 Replaced OxygenSensor 20 1,991 P0304 Replaced Spark Plug Wire, Spark Plug 21 1,963P0171, Replaced Fuel Filter P0174 22 1,952 P0306 Replaced Ignition Coil23 1,899 P0128 Replaced Thermostat Housing, Engine Coolant Thermostat 241,824 P0125 Replaced Engine Coolant Thermostat 25 1,783 P0031 ReplacedOxygen Sensor

Table 1 can be modified to include a separate column for otherattributes as well. The other attributes can identify RO attributes suchas, but not limited to, a customer complaint, a date, or a laboroperation code (LOC). As an example, the customer complaint can include,but is not limited to, terms such as rattles, won't start, and vibrates.Auto-generated repair-hints for those example customer complaint termscan include repair hints identifying a way to stop a vehicle fromrattling, a way to fix a vehicle that does not start, and a way to stopa vehicle from vibrating, respectively.

Table 2 below shows an example of data included on 25 of the 2,088 ROassociated with the RO cluster ID 18 shown in Table 1. The RO data inTable 2 includes an RO identifier that can, for example, be assigned bya VRT or VRD system 102. The RO data in Table 2 also includesyear/make/model/engine attributes associated with each RO.

TABLE 2 RO ID Year Make Model Engine 2197 1999 Cadillac Catera 3.0 L V6,VIN (R) 9277 1998 Mercury Grand Marquis 4.6 L V8, VIN (W) GS 1156 2002Ford Pickup F150 4.2 L, V6 VIN (2) 6978 2003 Ford Taurus SE 3.0 L V6,VIN (U) 7923 1999 Ford Pickup F150 4.6 L V8, VIN (W) 5074 2000 InfinitiI30 3.0 L V6, VIN (C) 5640 1997 Ford Cutaway E350 6.8 L, V10, VIN (S)1037 2002 Land Range Rover 4.6 L, V8, VIN (4) Rover HSE 1509 2002 FordExplorer 4.0 L, V6-245, SOHC 1673 2006 Ford Explorer 4.0 L, V6-245, SOHC2088 1998 Ford Cutaway E350 6.8 L, V10, VIN (S) 4692 2006 Ford PickupF250 5.4 L, V8 VIN (5) Super Duty 5183 1996 Mercury Grand Marquis 4.6 L,V8, VIN (W) GS MFI 6825 2000 Saturn LS2 3.0 L, V6, VIN (R) 8203 2001Hyundai XG300 3.0 L V6, VIN (D) 3915 1997 Ford Crown Victoria 4.6 L, V8,VIN (W) LX 7481 2001 Nissan Pathfinder SE 3.5 L, V6-3498, DOHC 7833 2007Chevrolet Silverado 6.0 L, V8, VIN (U) Classic 7976 1997 FordThunderbird LX 4.6 L, V8, VIN (W) 9892 2000 Nissan Maxima GLE 3.0 L V6,VIN (C) 0156 1999 Ford Econoline E150 4.6 L, V8, VIN (6) 1194 2002 FordPickup F150 4.2 L V6, VIN (2) 8797 2006 Ford Crown Victoria 4.6 L V8,VIN (W) LX 6321 2000 Ford Explorer 4.0 L V6, VIN (X) 6924 1998 FordRanger 4.0 L V6, VIN (X)

Some vehicle models are associated with a sub-model attribute. Somevehicle models are not associated with a sub-model attribute. Table 2can be modified to include a separate column to include sub-modelattributes for vehicles that are associated with a sub-model attribute.As an example, RO ID 7923 pertains to a Ford Pickup F150 make and model.The term “F150” can be referred to as a sub-model attribute. Othersub-model attributes for Ford Pickup models can include the “F250” and“F350” sub-model attributes. A sub-model attribute can be included on anRO. Searching for RO or repair-hints based on a sub-model in addition toY/M/M/E attributes can lead to search results having RO or repair-hintsassociated with a particular sub-model, but not the other sub-model(s)of a particular vehicle having particular Y/M/M/E attributes. The “S”within Y/M/M/S/E can represent a sub-model attribute.

Table 2 can be modified to include a separate column for otherattributes as well. The other attributes can identify system (Sys)attributes such as, but not limited to, a transmission attribute, asuspension attribute, and an audio system attribute. A set of attributesincluding a system attribute can be referred to as Y/M/M/E/Sysattributes.

Vehicle leverage data 224 can include computer-readable data thatidentifies different vehicle models built on a common vehicle platform.Vehicles built on a common vehicle platform can have many similaritiesincluding the use of common parts or part numbers. Vehicles built on acommon platform can experience similar vehicle symptoms that arise forsimilar reasons, such as failure of a part common to vehicles built onthe common vehicle platform. Table 3 shows an example of data that canbe stored as vehicle leverage data 224.

Processor 204 can generate an RO cluster that covers multiple vehiclemodels, such as the three vehicle models of VLD-3 shown in Table 3. IfRO 214 includes 100 RO for the Chevrolet Lumina APV model between1990-1996 and a given repair condition, 150 RO for the Pontiac TranSport models between 1990-1996 and the given problem, and 40 RO for theOldsmobile Silhouette model between 1990-1196 and the given problem,processor 204 can generate three separate RO clusters for the 290 RO ora single RO cluster for the 290 RO. A greater quantity of RO canindicate a greater likelihood of a successful repair of the givenproblem.

TABLE 3 Vehicle Leverage Data Identifier Model (VLD ID) Vehicle ModelsYear(s) Exceptions VLD-1 Cadillac Escalade, 2011-2013 GMC Yukon usesChevrolet Tahoe, Chevrolet hi-capacity Suburban, GMC Yukon radiatorVLD-2 Chevrolet Lumina APV, 1990-1996 N.A. Pontiac Trans Sport,Oldsmobile Silhouette VLD-3 Buick Regal, Oldsmobile 1998-2002 N.A.Intrigue VLD-4 Ford Expedition, 2008-2014 Lincoln Navigator LincolnNavigator uses aluminum cylinder heads

Processor 204 can use the exception data within vehicle leverage data224 to exclude RO pertaining to certain vehicle models from an ROcluster associated with a group of vehicles built on a common platform.For the exception data in Table 3, since the GMC Yukon uses a differentradiator than the Cadillac Escalade, the Chevrolet Tahoe, and theChevrolet Suburban, an RO cluster pertaining to a radiator for a GMCYukon may not be grouped with an RO cluster pertaining to a radiator onCadillac Escalades, Chevrolet Tahoes, and Chevrolet Suburbans.

Parts leverage data 226 can include data that identifies differentvehicle models that use a common part produced by one or more part(s)manufacturer. For purposes of this description, a common part is a partthat can be used in either of two or more vehicle models withoutaltering the part or any of the two or more vehicles to use the commonpart. Various references to a common part, such as a part number or partname, used by any or all of the part(s) manufacturer and themanufacturer(s) of the different vehicle models can be used. Vehiclemodels using a common part can experience similar vehicle symptoms thatarise for similar reasons, such as failure of the common part. Table 4shows an example of data that can be stored as parts leverage data 226.

TABLE 4 Common Vehicle Part Common Vehicle Model Part(s) IdentifierVehicle Part Models Year(s) manufacturer PLD-1 Coolant Cadillac 2012Delco Parts, temperature Escalade Inc. sensor PLD-1 Coolant Chevrolet2012 Delco Parts, temperature Tahoe Inc. sensor PLD-1 Coolant Chevrolet2012 Delco Parts, temperature Suburban Inc. sensor PLD-2 Fuel Honda 2013ACME, Inc. injector(s) Accord PLD-2 Fuel Honda 2013 ACME, Inc.injector(s) Civic

Processor 204 can generate an RO cluster that covers a common vehiclepart and multiple vehicle models, such as the coolant temperature sensorand three vehicle models of PLD-1 shown in Table 4. If RO 214 includes30 RO for the 2012 Cadillac Escalade model and the coolant temperaturesensor, 40 RO for the 2012 Chevrolet Tahoe model and the coolanttemperature sensor, and 20 RO for the 2012 Chevrolet Suburban model andthe coolant temperature sensor, processor 204 can generate threeseparate RO clusters for the 70 RO or a single RO cluster for the 70 RO.A greater quantity of RO can indicate a greater likelihood of occurrenceof a successful repair of a given problem arising from the coolanttemperature sensor.

CRPI 218 can include program instructions executable by processor 204 tocarry out functions described herein or performable by VRD system 200.CRPI 218 can include program instructions that are executable to parsedata from an original RO stored within RO 214 and to identify theservice procedure information, vehicle identification, and parts usageinformation from the original RO for use in generating a presentable ROor to increment a count of a cluster size if a presentable RO pertainingto the original RO has already been generated, or to decrement a clustersize if processor 204 transfers an RO from one cluster to a differentcluster.

CRPI 218 can include program instructions executable by processor 204 togenerate, for each auto-generated repair-hint and based on the RO storedin RO 214, meta-data associated with at least one set of search terms.Meta-data 222 can include meta-data generated by processor 204 based theinformation listed on original RO 400 including, but not limited to theLOC and a definition of the LOC.

CRPI 218 can include program instructions executable by processor 204 todetermine that words or phrases within service procedure information,such as service procedure information 418, 420, or 422, are within oneor more taxonomy groups of taxonomy term database 216, and to associate(e.g., relate) that service procedure information with the one or moretaxonomy groups. The service procedure information associated with anygiven taxonomy group can be part of a new RO cluster or additionalservice procedure information to be added to an RO cluster or to modifyan RO cluster.

CRPI 218 can include program instructions executable by processor 204 toperform any one or more of the operations, functions, or actionsillustrated in blocks 602-610 in FIG. 6 and as described below in thisdescription.

Text strings 228 can include strings of text (e.g., two or more words,numbers or symbols). A text string can include one or more gaps forinserting meta-data to complete the text string. A text string caninclude a complete text string without any gaps. Processor 204 canselect one or more text strings to associate with a set of terms (e.g.,search terms) that can be entered or received to search for a repairhint of repair hints 220. Processor 204 can select the meta-data toinsert into the gap(s) of a text string. Text strings 228 can includetext strings entered by user interface 206. Text strings 228 can includetext strings received by network interface 208.

Search terms 230 can include various sets of search terms. A set ofsearch terms can include vehicle-ID search terms 232 or a symptomcriterion 234. A first example set of search terms can include searchterms received by network interface 208 as part of a request for arepair hint. The first example set of search terms can include searchterms that are non-standard terms in taxonomy terms database 216 and canbe referred to as non-standard search terms (NSST). Processor 204 canidentify, within taxonomy term database 216, standard terms that matchthe search terms received by network interface 208 and then use anystandard terms included within the received search terms or identifiedfrom taxonomy term database 216 to search for a repair hint. Thenon-standard search terms stored as part of search terms 230 cansubsequently be reviewed by processor 204 or a human using RO manualentry device 202 or user interface 206 for inclusion as part of taxonomyterm database 216.

A second example set of search terms can include standard sets of searchterms and can be referred to as standard search terms (SST). A standardset of search terms can include standard vehicle-ID search terms, suchas Y/M/M/E attributes, defined in taxonomy term database 216 andstandard symptom criterion defined in taxonomy term database 216.Processor 204 can associate one or more standard sets of search termswith a repair hint or a repair order. A set of search terms associatedwith a repair hint or repair order can be stored as meta-data associatedwith that repair hint or repair order. Taxonomy term database 216 caninclude search terms 230. The second example set of search terms 230 canbe associated with one more sets of search terms like the first exampleset of search terms.

Table 5 shows an example of search terms that can be stored in searchterms 230. NSST-227 is associated with SST-15. SST-15 is associated withRO ID 3915. Repair hint 510 on RO ID 3915 can be identified in responseto receiving NSST-227, determining that SST-15 is associated withNSST-227, and determining RO ID 3915 is associated with SST-15. SST-1456is a set of standard search terms having symptom criterion common toSST-15 and SST-1456, and a Y/M/M/E attribute that differs from theY/M/M/E for SST-15 only by the model years (i.e., 2000 instead of 1999).SST-15 and SST-1456 are both associated with RO ID 3915. Thisassociation can be determined based on vehicle leverage data 224 orparts leverage data 226.

TABLE 5 Search Terms Y/M/M/E Symptom Criterion Associations NSST-227 97Ford Crown Emissions and MAF SST-15 Vic. 8 cyl. failed. DTC P171 P174.SST-15 1999/Ford/Crown Pcode: P0171, P0174 RO ID 3915 Victoria/4.6 LComponent: MAF sensor NSST-227 V8 (W) Work Requested: failed stateemissions certification SST-1456 2000/Ford/Crown Pcode: P0171, P0174 ROID 3915 Victoria/4.6 L Component: MAF sensor V8 (W) Work Requested:failed state emissions certification

The vehicle-ID search terms 232 is one example of search terms that canbe included within search terms 230. Vehicle-ID search terms 232 caninclude various selectable attributes. For example, the attributes ofvehicle-ID search terms 232 can include Y/M/M/E attributes. As anotherexample, the attributes of vehicle-ID search terms 232 can includeYear/Make/Model/Sub-model/Engine (Y/M/M/S/E) attributes as discussedwith respect to Table 2. As another example, the attributes ofvehicle-ID search terms 232 can include Year/Make/Model/Engine/System(Y/M/M/E/Sys) attributes. As another example, the attributes ofvehicle-ID search terms 232 can includeYear/Make/Model/Sub-model/Engine/System (Y/M/M/S/E/Sys) attributes.

The system (Sys) attribute vehicle-ID search terms 232 can indicate orrepresent a system (e.g., one or more systems) or a component (e.g., oneor more components) within a vehicle. As an example, the system orcomponent within the vehicle can identify (i) a powertrain transmissionwithin the vehicle (e.g., a 4-speed automatic transmission withover-drive), (ii) a rear differential within the vehicle (e.g., a reardifferential with a 4.11:1 gear ratio), (iii) an electric alternatorwithin the vehicle (e.g., a 100 ampere alternator), (iv) a heater,ventilation, and air-conditioning (HVAC) system installed within thevehicle (e.g., a dual-zone (e.g., a driver side and passenger side) HVACsystem), or some other system or component installed within, attachedto, or other otherwise operating on or in the vehicle.

The order of any of the vehicle-ID search terms 232 described herein canbe rearranged as desired. For example, the order of the Y/M/M/Eattributes could be rearranged as Make/Model/Engine/Year (M/M/E/Y)attributes or in another arrangement.

FIG. 5 shows an example content of a presentable RO 500 including an ROidentifier 502, RO timing information 504, RO vehicle identifier 506, avehicle service request 508, an auto-generated repair-hint 510,meta-data 512, and a usage indicator 514. Presentable RO 500 is based onservice procedure information 418 an original RO 400. RO identifier 502is “3915,” which is also shown in the seventeenth row of Table 2. ROtiming information 504 includes a year designator (i.e., 2009) toindicate a time that pertains to RO ID 3915. That time can indicate, forexample, when original RO 400 was written, completed, or submitted toVRD system 102. RO timing information could include other or differenttime information such as a day, month, or hour-of-a-day. RO vehicleidentifier 506 includes the year/make/model/engine attributes shown inthe seventeenth row of Table 2 for RO ID 3915. Additional or otherattributes of the given vehicle identified on original RO 400 can beincluded on presentable RO 500.

Presentable RO 500 includes elements in or based on original RO 400.Presentable RO 500 can be stored within data storage device 210 with oras part of original RO 400. Additionally or alternatively, presentableRO 500 can be stored separately and distinctly from original RO 400.

Vehicle service request 508 includes information pertaining to a vehicleservice request on an RO within RO 214. Vehicle service request 508 caninclude one or more text strings from text strings 228. As an example,each sentence within vehicle service request 508 can be a separate textstring. For example, a text string can include the text “Customer statesthe vehicle has [insert customer complaint].” The text within the squarebrackets (i.e., [ ]) identifies meta-data or a taxonomy term to beinserted to complete the text string. The portion of a text stringwithin the square brackets can be referred to as a “text string gap” ormore simply, “a gap.” Processor 204 can select the meta-data or thetaxonomy term based on information included on an original RO pertainingto RO ID 3915 received at VRD system 102. The text string “Pleasediagnose and advise” is an example of a text string without any gaps inwhich text is to be inserted to complete the text string. The term “MAFsensor” in the text string “Customer states please replace the MAFsensor” can be selected by processor to insert into the text string frommeta-data 512.

Auto-generated repair-hint 510 can include one or more text strings fromtext strings 228. As an example, each sentence within auto-generatedrepair-hint 510 can be a separate text string. For example, a textstring can include the text “Technician scan tested and verified the DTC[insert first Pcode] and DTC [insert second Pcode].” Processor 204 canselect the DTC (e.g., Pcode) identifiers “P0171” and “P0174” frommeta-data 512 to complete the text string by inserting those DTC (e.g.,Pcode) identifiers into the text string gaps. Processor 204 can selectthe meta-data based on information, such as a LOC, included on anoriginal RO pertaining to RO ID 3915 received at VRD system 102.

As another example, a text string can include multiple sentences withinauto-generated repair-hint 510, such as all of the sentences, but thefirst sentence, within auto-generated repair-hint 510. Processor 204 canselect fuel pump pressure readings (e.g., 30 and 40) to insert withinthe second sentence of that text string, and to select a component name(e.g., MAF sensor) from meta-data 512 or taxonomy term database 216 toinsert in the 4^(th) through 9^(th) sentences of the multiple-sentencetext string. Those inserted terms are underlined within FIG. 5 .

Meta-data 512 can be stored with presentable RO 500 within RO 214.Additionally or alternatively, meta-data 512 can be stored withinmeta-data 222 along with a tag or reference to presentable RO 500.

Usage indicator 514 indicates a distance in miles associated with RO500. Usage indicator 514 can be used by processor 204 to determinewhether to select auto-generated repair-hint 510 when searching for arepair-hint based on a set of search terms.

Next, FIG. 3 is a block diagram showing details of example a vehiclerepair tool (VRT) 300. VRT 300 can include or be arranged as a machine.VRT 300 includes a user interface 302, a processor 304, a networkinterface 306, and a data storage device 308, all of which can be linkedtogether via a system bus, network, or other connection mechanism 310.One or more of the VRT shown in FIG. 1 can be arranged like VRT 300. VRT300 can be used within system 100 like any of the VRT shown in FIG. 1 .

Processor 304 can be configured to execute computer-readable programinstructions, such as computer-readable program instructions (CRPI) 312stored within data storage device 308. For purposes of this description,processor 304 executing CRPI 312 to perform some function describedherein can include executing a portion of CRPI 312 or the entirety ofCRPI 312. Executing a portion or the entirety of CRPI 312 can includeexecuting some of the computer-readable program instructions multipletimes.

Data storage device 308 can include a non-transitory computer-readablestorage medium (i.e., two or more computer-readable storage mediums)readable by processor 304. The or each non-transitory computer-readablestorage medium can include volatile or non-volatile storage components,such as optical, magnetic, organic or other memory or disc storage,which can be integrated in whole or in part with a processor 304.

User interface 302 can include an interface to components that areconfigured to enter data or information into VRT 300 or to componentsthat are configured to present data or information output by VRT 300.Any of those components can be referred to as a VRT user interfacecomponent. User interface 302 can include one or more audio/visual portsor communication ports that connect to a VRT user interface component bya wired or wireless user interface communication link. Data orinformation entered into VRT 300 by user interface 302 can include dataor information for preparing an RO, such as original RO 400.

User interface 302 can include one or more of the VRT user interfacecomponents. As an example, the VRT user interface components can includean infrared remote control device, a display device, a loud speakerconfigured to convert electrical signals to audible sounds, a keyboard,a touch screen, a pointing device, such as a computer mouse, or someother component for generating signals to enter data or information intoVRT 300 or to present data or information output by user interface 302.User interface 302 can include a transmitter or transceiver to providethe data or information to another VRT user interface component.

Network interface 306 can include an interface to one or morecommunication networks, such as network 104. For use with wirelesscommunication networks, network interface 306 can include one or moreantennas for transmitting or receiving wireless communications. Networkinterface 306 can include one or more communication ports configured toconnect to a wired communication link of a network. Examples of thewired communication link are listed elsewhere herein. Network interface306 can include a network controller including a transmitter, areceiver, or a transceiver. The transmitter or transceiver can providedata or information to a communication port for transmission as networkcommunications over the connected network. The receiver or transceivercan receive data or information received at a communication port fromthe connected network. The data or information provided by networkinterface 306 to the network can include an RO.

CRPI 312 can include program instructions for generating an RO, such asoriginal RO 400, based on data input by user interface 302 or a userinterface component thereof. CRPI 312 can include program instructionsfor performing diagnostic functions for diagnosing a vehicle identifiedon an RO. As an example, performing the diagnostic functions can includechecking a diagnostic trouble code (DTC), such as a DTC 117, asidentified in section 428 of original RO 400. CRPI 312 can includeprogram instructions for (i) displaying, by user interface 302,vehicle-ID attributes selectable to form a set of search terms, symptomcriterion selectable to form part of the set of search terms, and afield for entering a usage indicator. (ii) receiving a selection of theset of search terms, (iii) providing the selected set of search terms tonetwork interface 306 for transmission of the selected search terms toVRD system 102, (iv) receiving, by network interface 306, a repair hint,such as an auto-generated repair-hint, from VRD system 102, and (v)displaying the received repair hint using user interface 302.

A VRT, such as VRT 300 or any of the VRT shown in FIG. 1 , can include,or be configured as, a smartphone, a tablet device, a laptop computer, adesktop computer, or an embedded computing device, such as the VERDICT™Diagnostic and Information System and the VERSUS® PRO IntegratedDiagnostic and Information System, both of which are available fromSnap-on Incorporated, Kenosha, Wis. Accordingly, a VRT can also includecomputer-readable program instructions to perform features such as, butnot limited to, guided component tests, an online expert forum,electrical measurements, waveform capture, displaying vehicle records,etc.

III. Example Operation

FIG. 6 is a flowchart illustrating a method 600, according to an exampleimplementation. Method 600 shown in FIG. 6 (and other processes andmethods disclosed herein) presents a method that can be implementedwithin an arrangement involving, for example, system 100, VRD system200, and/or VRT 300 (or more particularly by one or more components orsubsystems thereof, such as by a processor and a (e.g., non-transitoryor transitory) computer-readable medium having instructions that areexecutable to cause the device to perform functions described herein).Additionally or alternatively, method 600 may be implemented within anyother arrangements and systems.

Method 600 and other processes and methods disclosed herein may includeone or more operations, functions, or actions as illustrated by one ormore of blocks 602-610. Although the blocks are illustrated insequential order, these blocks may also be performed in parallel, and/orin a different order than those described herein. Also, the variousblocks may be combined into fewer blocks, divided into additionalblocks, and/or removed based upon the desired implementation.

In addition, for the method 600 and other processes and methodsdisclosed herein, the flowchart shows functionality and operation of onepossible implementation of present implementations. In this regard, eachblock may represent a module, a segment, or a portion of program code,which includes one or more instructions executable by a processor forimplementing specific logical functions or steps in the process. Theprogram code may be stored on any type of computer-readable medium, forexample, such as a storage device including a disk or hard drive. Thecomputer readable medium may include non-transitory computer-readablemedium, for example, such as computer-readable media that stores datafor short periods of time like register memory, processor cache andRandom Access Memory (RAM). The computer-readable medium may alsoinclude non-transitory media, such as secondary or persistent long termstorage, like read only memory (ROM), optical or magnetic disks,compact-disc read only memory (CD-ROM), for example. Thecomputer-readable media may also be any other volatile or non-volatilestorage systems. The computer-readable medium may be considered acomputer readable storage medium, for example, or a tangible storagedevice. In addition, for the method 600 and other processes and methodsdisclosed herein, each block in FIG. 6 may represent circuitry that iswired to perform the specific logical functions in the process.

At block 602, method 600 involves determining, by at least oneprocessor, that a particular computer-readable vehicle repair order (RO)corresponds to an existing cluster of ROs due to the particular ROincluding RO data that refers to a particular vehicle symptom, where theexisting cluster is arranged to contain ROs that correspond to theparticular vehicle symptom, and where the particular RO comprises atleast a first RO portion and a second RO portion.

In an example implementation, a processor (e.g., processor 204) may beconfigured to evaluate a particular RO. This particular RO may be one ofthe ROs 214 stored in data storage device 210, may be a new RO receivedfrom RO manual entry device 202, or may be a new RO received fromnetwork interface 208 by way of network 104, among other possibilities.Moreover, this particular RO can be an original RO, such as an ROgenerated by a VRT shown in FIG. 1 or entered using RO manual entrydevice 202, or the particular RO may be a presentable RO generated byVRD system 200.

While evaluating the particular RO, the processor 204 may determine thatthe particular RO corresponds to an existing cluster of ROs. Thisexisting cluster may be a cluster within which the processor 204 haspreviously associated one or more ROs. Hence, the existing cluster mayhave cluster size of at least 1 (one), thereby indicating that at leastone RO has been associated with this existing cluster. The at least oneRO that has been associated with this existing cluster may be theparticular RO at issue. That is, the particular RO may have already beenassociated with the existing cluster and thus the existing clusteralready contains the particular RO. Alternatively, the particular RO maybe a new RO that has not yet been added to (e.g., associated with) theexisting cluster. In this case, the processor 204 may determine that theparticular RO corresponds to the existing cluster and should thus beadded to the existing cluster so as to increase the cluster size.

More specifically, the existing cluster can be associated withparticular defined RO attributes, such as with a particular vehiclesymptom for instance. This particular vehicle symptom may be aparticular DTC, such as DTC P0171 shown in FIG. 4 for example.Alternatively, this particular vehicle symptom may be one of the symptomcriterion 234, such as “Emissions and MAF failed” shown in Table 5 abovefor example. The particular vehicle symptom could also take on otherforms. In either case, the existing cluster may be arranged to containROs that correspond to the particular defined RO attributes, such as tothe particular vehicle symptom for instance.

In an example arrangement, the processor 204 may determine that theparticular RO corresponds to the existing cluster in one of variousways. For example, if the existing cluster already contains theparticular RO, the processor 204 may determine that the particular ROcorresponds to the existing cluster simply by determining that theexisting cluster already contains the particular RO. For instance, thismay involve the processor 204 determining the cluster ID associated withthe particular RO. To do so, the processor 204 may refer to meta-data222, which may include meta-data associating certain ROs with certainclusters (e.g., by way of certain cluster IDs). As such, a cluster maycontain a certain RO when this certain RO is associated with thecluster, such as by way of being associated with an ID of the clusterfor instance.

In another example, whether the existing cluster already contains theparticular RO or whether the particular RO is a new RO, the processor204 may determine that the particular RO corresponds to the existingcluster by determining that the particular RO includes RO attributesthat match the above-mentioned particular defined RO attributes. To doso, processor 204 can search the text, symbols or other content on theparticular RO or the meta-data associated with the particular RO inorder to associate the particular RO within the existing cluster. Forinstance, the processor 204 may determine that the particular ROincludes content related to the particular vehicle symptom and may thusassociate the particular RO with the existing cluster due to theexisting cluster being arranged to contain ROs that correspond to theparticular vehicle symptom. Other examples are also possible.

In some cases, the existing cluster being arranged to contain ROs thatcorrespond to the particular vehicle symptoms may specifically involvearranging the existing cluster to contain ROs that correspond to theparticular vehicle symptom without also corresponding to a specificvehicle component (or to a corrective action being done to the specificvehicle component in order to resolve the particular vehicle symptom).ROs contained within such a cluster may thus contain content related tothe particular vehicle symptom but may not contain content that isrepresentative of a specific vehicle component. Rather, as furtherdiscussed in detail below, an RO contained within such a cluster mayperhaps contain content that is related to a non-specific vehiclecomponent. In such cases, the processor 204 may determine that theparticular RO corresponds to the existing cluster by determining thatthe particular RO contains content related to the particular vehiclesymptom but does not contain content that is representative of aspecific vehicle component.

Table 6 shows data identifying three clusters identified with ID 26through 28, inclusive. The cluster size indicates how many RO have beenassociated with the respective cluster. The cluster size can be modifiedas or after additional RO are added to RO 214 or after an RO istransferred from one cluster to a different cluster. Table 6 showsexamples of DTC, Action, and component attributes associated with eachrespective RO cluster. Of course, as noted above in relation to Table 1,Table 6 can be modified to include a separate column for otherattributes as well. The other attributes can identify RO attributes suchas, but not limited to, a customer complaint, a date, or a laboroperation code (LOC).

TABLE 6 Cluster Cluster ID Size DTC Action Component(s) 26 1,254 P0101 —— 27 1,516 P0503, P0504 — — 28 984 P0906 — —

As shown, these example clusters are each arranged to contain ROs thatcorrespond to at least one particular vehicle symptom without alsocorresponding to an action and/or to a component attributes. Forinstance, the cluster having a cluster ID of 26 is associated with asingle vehicle symptom taking the form of DTC P0101. Whereas, thecluster having a cluster ID of 27 is associated with two vehiclesymptoms that respectively take the form of DTC P0503 and DTC P0504. Ofcourse, an example cluster could be associated with two or more vehiclesymptoms. In either case, the above-mentioned existing cluster could beone of the example clusters shown in Table 6, among other possibleclusters.

With this example arrangement, the above-mentioned particular RO may beone of the ROs in one of the clusters shown in Table 6. For instance,the particular RO may be one of the ROs associated with RO cluster ID26. To illustrate, Table 7 next shows an example of data included onfive of the 1,254 ROs associated with the RO cluster ID 26 shown inTable 6. The RO data in Table 7 includes an RO identifier that can, forexample, be assigned by a VRT or VRD system 102. The RO data in Table 7also includes year/make/model/engine attributes associated with each RO.Of course, as noted above in relation to Table 2, Table 7 can bemodified to include a separate column to include other attributes, suchas sub-model attributes for vehicles that are associated with asub-model attribute.

TABLE 7 RO ID Year Make Model Engine 6478 2001 Infiniti I30 3.0 L V6,VIN (C) 2224 1999 Ford Cutaway E350 6.8 L, V10, VIN (S) 4390 2005 FordPickup F250 5.4 L, V8 VIN (5) Super Duty 1273 2001 Hyundai XG300 3.0 LV6, VIN (D) 3815 2001 Nissan Maxima GLE 3.0 L V6, VIN (C)

Further, as noted, the particular RO at issue may include at least firstand second RO portions. In some cases, the first RO portion may bedifferent from the second RO portion. In other cases, the first ROportion may be the same as the second RO portion. In either case, an ROportion may generally refer to an area that is found within an RO andthat contains a set of computer-readable data of a certainclassification or category.

By way of example, an RO portion may be a service provider identifier402 portion, a date of service identifier 404 portion, a customerindicator 406 portion, a vehicle information 408 portion, a vehicleservice request portion (e.g., vehicle service requests 410, 412, and414), a parts information 416 portion, a service procedure informationportion (e.g., service procedure information 418, 420, and 422), and avehicle-usage indicator 430 portion. In other examples, an RO portionmay be a VIN 432 found within vehicle information 408, or a distinct ROsection (e.g., section 424) found within service procedure information(e.g., service procedure information 418). In yet other examples, an ROportion may be an LOC, a DTC, a vehicle symptom, a labor line foundwithin a distinct RO section and specifying a corrective action taken toresolve a vehicle symptom, or a parts line found within partsinformation and specifying information about a certain part (e.g. FIG. 4showing a part line of an MAF sensor that has an associated part number6012980 and is priced at $89.99). Other examples are possible as well.

At block 604, method 600 involves determining, by the at least oneprocessor, that the first RO portion includes first data representativeof a non-specific vehicle component.

According to various implementations, a specific vehicle component maybe represented by one of the terms (e.g., words or phrases) found withina certain taxonomy group of the taxonomy term database 216. This certaintaxonomy group may essentially provide for a vehicle component database238 including a plurality of computer-readable vehicle component terms.As such, if the processor 204 determines that a given text string (e.g.,found in an RO portion) matches a distinct vehicle component term fromamong the plurality of vehicle component terms found in the vehiclecomponent database 238, then the processor 204 may responsivelydetermine that this given text string is representative of a specificvehicle component.

By way of example, the processor 204 may evaluate service procedureinformation 422 and, while doing so, may determine that serviceprocedure information 422 of an RO includes a text string of “coolantsensor”. The processor 204 may then refer to the vehicle componentdatabase 238 to determine whether this text string matches just one ofthe vehicle component terms found in the vehicle component database 238,substantially matches two or more vehicle component terms found in thevehicle component database 238, or does not match any of the vehiclecomponent terms found in the vehicle component database 238. In thisexample, the processor 204 may make a determination that the text stringat issue matches just one of the vehicle component terms found in thevehicle component database 238, such as by determining that the sequenceof letters/number/symbols/gaps within the text string (e.g., coolantsensor) is exactly the same as a sequence of letters/number/symbols/gapswithin a distinct vehicle component term, for instance. Thisdetermination may then serve as an indication to the processor 204 thatthe text string of “coolant sensor” is representative of a specificvehicle component.

As such, if an RO contains content related to the particular vehiclesymptom as well as content that is related to a specific vehiclecomponent, then this RO may be associated with a cluster that isarranged to contain ROs corresponding to the particular vehicle symptomand to the specific vehicle component. Nonetheless, such content relatedto a specific vehicle component may not have been found within theparticular RO at issue. As a result, the particular RO may be initiallyassociated with the existing cluster that is arranged to contain ROscorresponding to the particular vehicle symptom but not to a specificvehicle component.

In some cases, however, the particular RO may contain content that isrelated to a non-specific vehicle component, which may ultimately beused to determine a specific vehicle component as further discussedbelow. According to an example implementation, the processor 204 maythus evaluate the particular RO to determine whether the particular ROincludes data representative of a non-specific vehicle component. Thisnon-specific vehicle component may be represented by a term (e.g., wordor phrase) found within several vehicle component terms of the vehiclecomponent database 238. As a result, ambiguity may generally arise as towhich of these several vehicle component terms is the actual vehiclecomponent being referred to in the particular RO, thereby resulting inissues such as incorrect association with the existing cluster and/or arepair technician incorrectly interpreting the particular RO, amongother possible issues.

More specifically, the processor 204 may determine that a given textstring is found within the above-mentioned first RO portion and maydetermine that the given text string substantially matches two or morevehicle component terms from the vehicle component database 238. Inparticular, this may involve determining that the given text stringsubstantially matches at least a portion of each of these two or morevehicle component terms. As such, if the processor 204 determines that agiven text string (e.g., found in the first RO portion) substantiallymatches two or more vehicle component terms from among the plurality ofvehicle component terms found in the vehicle component database 238,then the processor 204 may responsively determine that this given textstring is representative of a non-specific vehicle component.

By way of example, the processor 204 may evaluate parts information 416and, while doing so, may determine that a parts line of partsinformation 416 includes a text string of “sensor”. The processor 204may then refer to the vehicle component database 238 to determinewhether this text string matches just one of the vehicle component termsfound in the vehicle component database 238, substantially matches twoor more vehicle component term found in the vehicle component database238, or does not match any of the vehicle component terms found in thevehicle component database 238. In this example, the processor 204 maymake a determination that the text string substantially matches two ormore vehicle component term found in the vehicle component database 238.For instance, the processor 204 may determine the vehicle componentdatabase 238 includes two hundred vehicle component terms each includingthe text string of “sensor”. Examples of such vehicle component termsmay include (without limitation): MAF sensor, coolant sensor,temperature sensor, light sensor, oxygen sensor, pressure sensor, andfuel sensor. As such, when the processor 204 makes this determination,then this determination may then serve as an indication to the processor204 that the text string of “sensor” is representative of a non-specificvehicle component. Other examples are possible as well.

At block 606, method 600 involves, in response to determining that thefirst RO portion includes the first data, the at least one processordetermining that the second RO portion includes second data that the atleast one processor can use to determine a specific vehicle componentassociated with the particular RO. And at block 608, method 600involves, in response to determining that the second RO portion includesthe second data, the at least one processor determining the specificvehicle component based on the first and second data.

According to an example implementation, once the processor 204determines that the first RO portion includes first data representativeof a non-specific vehicle component, the processor 204 may furtherevaluate the particular RO to determine whether the particular ROincludes any second data that the processor 204 can use as a basis todetermine a specific vehicle component, so as to ultimately eliminateany ambiguity arising from the non-specific vehicle component. In doingso, the processor 204 may evaluate second RO portion(s) of theparticular RO. In particular, these second RO portion(s) may be one ormore RO portions of the particular RO, such as a parts line and/or alabor line for instance. And in some cases, at least one of these one ormore RO portion could be the same as the first RO portion discussedabove.

In this example implementation, the processor 204 may carry out any ofseveral sets of operations in order to determine whether the particularRO includes such second data and to then use this second data incombination with the first data to determine the specific vehiclecomponent. When taking steps to determine the specific vehiclecomponent, the processor 204 may carry out one or more of these sets ofoperations and may also carry out these sets of operations in any order.Moreover, once the processor 204 carries out a set of operations thatresults in determination of a specific vehicle component, the processor204 may then responsively stop carrying out any further sets ofoperations. These sets of operations will now be described in moredetail.

In one case, the processor 204 may determine whether a particular DTCidentifier is associated with the particular RO. For example, theprocessor 204 may do so by determining the existing cluster associatedwith the particular RO, such as by determining the cluster ID associatedwith the particular RO. And the processor 204 may then determine whetherthe existing cluster is arranged to contain ROs that correspond to aparticular DTC identifier. If the processor 204 determines that theexisting cluster is arranged to contain ROs that correspond to aparticular DTC identifier, then the processor 204 may responsivelydetermine that the particular DTC identifier is associated with theparticular RO. Alternatively, the processor 204 may refer to a second ROportion and may determine that the second RO portion includes aparticular DTC identifier, such as by determining that a text stringfound within the second RO portion matches a DTC identifier from ataxonomy group that includes a plurality of DTC identifiers, forinstance.

Once the processor 204 determines a particular DTC identifier isassociated with the particular RO, the processor 204 may determine aspecific vehicle component based on the non-specific vehicle componentand the determined particular DTC identifier. To do so, the processor204 may statistically infer the specific vehicle component based on thenon-specific vehicle component and the determined particular DTCidentifier. In particular, the processor 204 may refer to the datastorage device 210 that contains a plurality of ROs 214. From among thisplurality of ROs 214, the processor 204 may recognize ROs that are eachassociated with the particular DTC identifier, such as ROs that includethe particular DTC identifier in the repair order text for instance. Theprocessor 204 may then determine that a threshold percentage (e.g.,established via manual engineering input and/or incorporated as part ofCRPI 218) of the recognized ROs include repair data referring to theparticular DTC identifier as well as to a given vehicle component thatis related to the non-specific vehicle component. This given vehiclecomponent may be one of the above-mentioned vehicle component terms forwhich at least a portion of the term matches the text string of thenon-specific vehicle component. Once the processor 204 determines that athreshold percentage of the recognized ROs include such repair data, theprocessor 204 may responsively determine that the given vehiclecomponent is the specific vehicle component.

By way of example, the particular RO may include a non-specific vehiclecomponent of “sensor” and may be associated with a DTC of P0135. In thisexample, the processor 204 may determine a percentage of the ROs thateach includes repair data referring to P0135 as well as to a vehiclecomponent term having a portion which includes the text string of“sensor”. For instance, the processor 204 may determine that eightypercent of ROs include repair data referring to P0135 as well as to anoxygen sensor. Then, the processor 204 may determine that thispercentage exceeds a threshold percentage of seventy percent and maythus responsively determine that the specific vehicle component is anoxygen sensor. Other examples are possible.

In another aspect, the processor 204 may also use vehicle attributes asan additional basis for determining the specific vehicle component. Inparticular, the processor 204 may determine that the particular ROincludes specific data that refers to a particular vehicle havingparticular vehicle attributes. The processor 204 may do so in variousways. In one example, the processor 204 could refer to meta-dataassociated with the particular RO in order to identify the particularvehicle as well as the particular vehicle attributes. In anotherexample, the processor 204 may refer to vehicle information 408, such byreferring to the vehicle identification number (VIN) 432 associated withthe particular vehicle and/or to the description of the particularvehicle. With this information, the processor 204 could then refer tovehicle-ID search terms 232 to determine one or more vehicle attributesassociated with the particular vehicle. These vehicle attributes mayinclude, but are not limited to: (i) a vehicle year attribute, (ii) avehicle make attribute, (iii) a vehicle model attribute, (iv) a vehicleengine attribute, (v) a vehicle system attribute, (vi) avehicle-year-make-model attribute, (vii) avehicle-year-make-model-submodel attribute, (viii) a vehicle enginecode, (ix) a vehicle drive type, and (x) a vehicle fuel system type.

Once processor 204 determines that the particular RO includes specificdata that refers to a particular vehicle having particular vehicleattributes, the processor 204 may determine that the particular vehicleis related to a set of one or more vehicles. The one or more vehicles ofthe set may all share one or more of the same vehicle attributes. Hence,the particular vehicle may be related to the set due to having the sameor similar vehicle attributes as those of one or more vehicles of theset. Moreover, the processor 204 may determine that the particularvehicle is related to the set based on an indication (e.g., storedwithin data storage device 210) that the particular vehicle is relatedto the set. For example, the processor 204 could do so by referring tovehicle leverage data 224 including computer-readable data thatidentifies different vehicle models built on a common vehicle platform.In another example, the processor 204 could do so by referring to partsleverage data 226 including data that identifies different vehiclemodels that use a common part produced by one or more part(s)manufacturer. Other examples are also possible.

As such, the processor 204 may identify ROs, from among the plurality ofROs 214, that include further data referring to at least one vehiclefrom the set. In particular, an RO could be associated with at least onevehicle from the set due to, for instance, having vehicle information408 that refers to at least one such vehicle from the set. The processor204 may identify such ROs in various ways. For example, the processor204 could refer to search terms 230 so as to identify one or more RO IDsassociated with a given vehicle from the set. The processor 204 could doso for some of or all of the vehicles from the set and could thusidentify a plurality of RO IDs associated with ROs that each have datareferring to at least one vehicle from the set.

From among these identified ROs, the processer 204 may then recognizeROs that are each associated with the particular DTC identifier and maythen take steps as discussed above in this case in order to determinethe specific vehicle component. In particular, the processor 204 maythen determine that a threshold percentage of the recognized ROs includerepair data referring to the particular DTC identifier as well as to agiven vehicle component that is related to the non-specific vehiclecomponent, and the processor 204 may then responsively determine thatthe given vehicle component is the specific vehicle component. In thismanner, the processor 204 may carry out the set of operations for thiscase only in the context of ROs that list vehicles having similarvehicle attributes to those of the particular vehicle at issue, therebystatistically increasing the probability of correctly inferring thespecific vehicle component.

In another case, the processor 204 may determine whether a particularvehicle symptom (e.g., other than a DTC identifier) is associated withthe particular RO. For example, the processor 204 may do so bydetermining the existing cluster associated with the particular RO, suchas by determining the cluster ID associated with the particular RO. Andthe processor 204 may then determine whether the existing cluster isarranged to contain ROs that correspond to a particular vehicle symptom.If the processor 204 determines that the existing cluster is arranged tocontain ROs that correspond to a particular vehicle symptom, then theprocessor 204 may responsively determine that the particular vehiclesymptom is associated with the particular RO. Alternatively, theprocessor 204 may refer to a second RO portion and may determine thatthe second RO portion includes symptom data representative of theparticular vehicle symptom, such as by determining that a text stringfound within the second RO portion matches a vehicle symptom from ataxonomy group that includes a plurality of vehicle symptoms, forinstance.

Once the processor 204 determines a particular vehicle symptom isassociated with the particular RO, the processor 204 may use thisparticular vehicle symptom as a basis for determining the specificvehicle component. As an initial matter, the processor 204 may determinethat that the particular vehicle symptom is associated with a particularvehicle system, such as a particular vehicle symptom having one or morevehicle components that may exhibit this particular vehicle symptom. Forexample, the processor 204 could do so by referring to meta-dataassociated with the particular vehicle symptom in order to identify thisparticular vehicle system. And once the processor 204 determines thatthat the particular vehicle symptom is associated with a particularvehicle system, the processor 204 may determine the specific vehiclecomponent based on the non-specific vehicle component and the particularvehicle system.

More specifically, the non-specific vehicle component may correspond toa first text string that is representative of the non-specific vehiclecomponent while the particular vehicle system may correspond to a secondtext string that is representative of the particular vehicle system.With this arrangement, the processor 204 may generate a particular textstring that is a combination of at least a portion of the first textstring and at least a portion of the second text string. Then, theprocessor 204 may refer to the vehicle component database 238 todetermine if the generated text string matches a distinct vehiclecomponent term from among the plurality of vehicle component terms foundin the vehicle component database 238. If the generated text stringmatches a distinct vehicle component term, then the processor 204 maydetermine that the generated text string is representative of thespecific vehicle component. Whereas, if the generated text string doesnot match a distinct vehicle component term, then the processor 204 maygenerate another text string, such as by using different respectiveportions of the first and second text strings. And the processor 204 maythen again repeat this set of operations for the newly generated textstring. In this manner, the processor 204 may continue generating textstrings until the processor 204 generates a text string that matches adistinct vehicle component term.

By way of example, the particular RO may include a non-specific vehiclecomponent of “clutch” and may be associated with a particular vehiclesymptom of “A/C inoperative” (with A/C referring to air conditioning).In this example, the processor 204 may refer to meta-data associatedwith “A/C inoperative” and may make a determination that “A/Cinoperative” is associated with an “A/C system”. Once the processor 204makes this determination, the processor 204 may then generate aparticular text string as discussed above. For instance, the processor204 may use a portion of the term “A/C system”, such as the portion of“A/C”. Also, the processor 204 may use the entirety of the term“clutch”. Then, the processor 204 may combine the portion “A/C” with theterm “clutch” to result in the text string of “A/C clutch”. With thisapproach, the processor 204 may then determine that “A/C clutch” matchesa distinct vehicle component term in the vehicle component database 238,thereby determining that “A/C clutch” is the specific vehicle component.

In yet another case, the processor 204 may determine whether theparticular RO (e.g., the second portion of the particular RO) includesreplacement data specifying a replaced vehicle component. For instance,such replacement data could be found in a labor line of the particularRO, among other possibilities. To determine whether the particular ROincludes replacement data, the processor 204 may determine whether theparticular RO includes replacement text that matches one of a pluralityof terms found in a taxonomy group of terms related to replacement, suchas terms including (without limitation): “replace”, “replaced”,“replacing”, “replacement”, “R/R” (R/R stands for “remove and replace”or “remove and repair”), or the like. If the processor 204 determinesthat the particular RO includes replacement text that matches one ofthese plurality of terms, the processor 204 may evaluate a term thatfollows (and/or a term that precedes) the replacement text in order todetermine whether the following term matches a distinct vehiclecomponent term from among the plurality of vehicle component terms foundin the vehicle component database 238. As such, if the processor 204determines that the following term matches a distinct vehicle componentterm, the processor 204 may then determine that the following termrefers to a replaced vehicle component.

In this case, the processor 204 may compare the replaced vehiclecomponent to the non-specific vehicle component in order to determinewhether the replaced vehicle component is the specific vehiclecomponent. In particular, the non-specific vehicle component maycorrespond to a first text string that is representative of thenon-specific vehicle component while the replaced vehicle component maycorrespond to a second text string that is representative of thereplaced vehicle component. With this arrangement, the processor 204 maydetermine whether the first text string matches at least a portion ofthe second text string. And if the processor 204 determines that thefirst text string matches at least a portion of the second text string,then the processor 204 may responsively determine that the replacedvehicle component is indeed the specific vehicle component.

By way of example, the particular RO may include a non-specific vehiclecomponent of “wiring harness” and may be associated with a DTC of P0335.In this example, the processor 204 may determine that a distinct ROsection has a labor line including the replacement text of “replaced”,which matches one of the plurality of terms related to replacement.Also, the processor 204 may then determine that the term “CKP wiringharness” follows the replacement text of “replaced” and that this termmatches a distinct vehicle component term. Subsequently, the processor204 may make a determination that the text string of “wiring harness”matches a portion of the text string of “CKP wiring harness”. And inresponse to making this determination, the processor 204 may determinethat the specific vehicle component is a CKP wiring harness. Otherexamples are possible as well.

In some cases, the processor 204 may determine that the particular RO(e.g., the first portion of the particular RO) includes replacement dataspecifying a replaced vehicle component, such as by using the techniquesdiscussed above. For instance, this replacement data could be found inany RO text field of the particular RO. Regardless, when determiningthat the particular RO include replacement data, the processor 204 maydetermine whether the replaced vehicle component matches a distinctvehicle component term from the plurality of vehicle component termsfound in the vehicle component database 238 or whether the replacementdata specifies replacement of a non-specific vehicle component. If theprocessor 204 determines that the replaced vehicle component matches adistinct vehicle component term, then the processor 204 may responsivelydetermine that the distinct vehicle component term is representative ofthe specific vehicle component. In contrast, if the processor 204determines that the replacement data actually specifies replacement of anon-specific vehicle component, then the processor 204 may responsivelyuse any set of operations discussed herein to determine the specificvehicle component.

By way of example, the processor 204 may determine that the particularRO is associated with a DTC of P0101 that there is no vehicle componentslisted on any parts line of the particular RO. However, the processor204 may determine that a distinct RO section includes the replacementtext of “replaced”, which matches one of the plurality of terms relatedto replacement. Also, the processor 204 may then determine that the term“sensor” follows the replacement text of “replaced” and that this termdoes not match a distinct vehicle component term, thereby indicatingthat the term is representative of a non-specific vehicle component.Responsively, the processor 204 may use at least one set of operationsdiscussed herein in order to determine a specific vehicle component andthus eliminate ambiguity associated with the term “sensor”. Forinstance, the processor 204 may determine that the particular RO isassociated with a DTC of P0101. In this instance, the processor 204 maydetermine that ninety percent of ROs in the database include repair datareferring to P0101 as well as to a Mass Airflow sensor. Then, theprocessor 204 may determine that this percentage exceeds a thresholdpercentage of seventy percent and may thus responsively determine thatthe replaced specific vehicle component is a Mass Airflow sensor. Otherexamples are possible.

In yet another case, the processor 204 may determine whether theparticular RO (e.g., the second portion of the particular RO) includesidentification data specifying a particular vehicle part numberassociated with the non-specific vehicle component. For instance, theprocessor 204 may refer to a parts line that includes the non-specificvehicle component and may determine whether this parts line includes aparticular vehicle part number. If the parts line includes theparticular vehicle part number, the processor 204 may refer to the datastorage device 210 in order to determine the specific vehicle component.

In particular, the data storage device 210 may contain a part numberdatabase 240 that includes mapping data that maps certain vehicle partnumbers to certain respective part number descriptions. With thisarrangement, the processor 204 may refer to the data storage device 210to determine whether the particular vehicle part number matches one ofthe vehicle part numbers found in the part number database 240. If theprocessor 204 determines that the particular vehicle part number indeedmatches one of the vehicle part numbers, the processor 204 may use themapping data to determine the respective part number description of theparticular vehicle part number. And this respective part numberdescription of the particular vehicle part number may include thespecific vehicle component. In this manner, the processor 204 maydetermine the specific vehicle component using a particular vehicle partnumber found in a parts line associated with the non-specific vehiclecomponent.

By way of example, the particular RO may include a non-specific vehiclecomponent of “sensor” on a given parts line. In this example, theprocessor 204 may determine that the given parts line also includes aparticular vehicle part number of “37980-RLF-003”. Accordingly, theprocessor 204 may refer to the data storage device 210 and may determinethat the particular vehicle part number of “37980-RLF-003” matches oneof the vehicle part numbers found in the part number database 240.Responsively, the processor 204 may then use the mapping data todetermine the respective part number description for the particularvehicle part number of “37980-RLF-003”. And the processor 204 may thendetermine that this respective part number description includesreference to a “Mass Air Flow sensor”, which matches one of the vehiclecomponent terms found in the vehicle component database 238. As such,the processor 204 may determine that the specific vehicle component is aMass Air Flow sensor. Other cases and examples are possible as well.

At block 610, method 600 involves, in response to determining thespecific vehicle component, the at least one processor adding theparticular RO to a different cluster of ROs, where the different clusteris arranged to contain ROs that correspond to the particular vehiclesymptom and to the specific vehicle component.

In an example implementation, the processor 204 may determine thespecific vehicle component and may then add this particular RO to adifferent cluster of ROs. This different cluster may be arranged tocontain ROs that have data corresponding to the above-mentionedparticular vehicle symptom as well as data corresponding to thedetermined specific vehicle component. For instance, a different clustermay contain ROs that correspond to a DTC of P0135 and to a determinedspecific vehicle component of “Oxygen Sensor”. In another instance, adifferent cluster may contain ROs that correspond to a particularvehicle symptom of “A/C inoperative” and to a determined specificvehicle component of “A/C clutch”. In yet another instance, a differentcluster may contain ROs that correspond to a DTC of P0335 and to adetermined specific vehicle component of “CKP wiring harness”. In yetanother instance, a different cluster may contain ROs that correspond toa DTC of P0101 and to a determined specific vehicle component of “MassAirflow sensor”. Of course, the different cluster could also involveother vehicle attributes and may thus be arranged to contain ROs thatcorrespond to the particular vehicle symptom, to the determined specificvehicle component, and to these other vehicle attributes.

In one example situation, as noted above, the processor 204 maydetermine that the particular RO is a new RO that is received by theprocessor 204 before the particular RO has been added to any of theexisting cluster and the different cluster. In this situation, theprocessor 204 may add the particular RO to both the existing cluster andto the different cluster. In another case, the processor 204 may add theparticular RO to the different cluster without also adding theparticular RO to the existing cluster. Other cases are also possible.

In another example situation, as noted above, the processor 204 maydetermine that the particular RO is already contained in the existingcluster. In this situation, the processor 204 may add the particular ROto the different cluster and may also keep the particular RO in theexisting cluster such that the particular RO is then contained in boththe existing cluster and the different cluster. In another case, theprocessor 204 may add the particular RO to the different cluster and mayalso remove the particular RO from the existing cluster such that theparticular RO is then contained in the different cluster and is nolonger contained in the existing cluster. Other cases are also possible.

FIGS. 7A to 7B illustrate example of movement of an RO between clusters.In particular, FIG. 7A shows an example cluster 700 containing ROs 704to 716 as well as example cluster 702 containing ROs 718 to 722. Asillustrated, RO 714 is being moved from cluster 700 to cluster 702.Then, after the RO 704 has been moved, FIG. 7A shows the cluster 702 ascontaining RO 714 and the cluster 700 as no longer containing RO 714.Further, FIGS. 8A to 8B illustrate example of addition of an RO to acluster. In particular, FIG. 8A shows an example cluster 800 containingROs 802 to 812. As illustrated, new RO 814 is being added to the cluster800. Then, after the new RO has been added to the cluster 800, FIG. 8Bshows the cluster 800 as containing RO 814. Note that clusters 700, 702,and 800 may be stored in clusters of data storage device 210 (e.g.,within clusters database 236 further discussed below). Otherillustrations are also possible.

In an example arrangement, the processor 204 may use various techniquesto add the particular RO to a cluster, to remove the particular RO froma cluster, to move the particular RO between clusters, and/or to keepthe particular RO in a cluster. In one case, to add the particular RO toa cluster, the processor 204 may generate meta-data to associate theparticular RO with an appropriate cluster ID and may then store thisgenerated meta-data (e.g., within meta-data 222 along with a tag orreference to the particular RO). In another case, to remove theparticular RO from a cluster, the processor 204 may (i) revise meta-datathat associates the particular RO with the cluster ID so as to indicatethat the particular RO is no longer associated with this cluster ID andmay then (ii) store this revised meta-data. Alternatively, to remove theparticular RO from a cluster, the processor 204 may remove meta-datathat associates the particular RO with the cluster ID such as byremoving this meta-data from data storage 210.

In yet another case, to move the particular RO between clusters, theprocessor 204 may revise meta-data that associates the particular ROwith a first cluster ID so as to indicate within the meta-data that theparticular RO is no longer associated with this first cluster ID and israther associated with a second cluster ID. Alternatively, to move theparticular RO between clusters, the processor 204 may generate meta-datato associate the particular RO with a first cluster ID (and may thenstore this generated meta-data) while also removing or revisingmeta-data that associates the particular RO with a second cluster ID inthe manner discussed above. In yet another case, to keep the particularRO in a cluster, the processor 204 may simply maintain any meta-datathat associates the particular RO with this cluster. Other cases arealso possible.

In an example implementation, the processor 204 could refer to the datastorage 210 to determine whether or not the data storage 210 containsthe different cluster. This may specifically involve the processor 204referring to a clusters database 236 within data storage 210 thatincludes clusters of ROs. In particular, the clusters database 236 mayinclude all existing IDs associated with clusters of ROs, clusterattributes associated with certain clusters of ROs, and/or meta-dataassociating certain ROs with certain clusters of ROs (additionally oralternatively, this meta-data could be included within meta-data 222 asdiscussed above), among others. With this arrangement, the processor 204may determine whether or not the data storage 210 contains a cluster IDfor a particular cluster that is arranged to contain ROs that correspondto the particular vehicle symptom and to the determined specific vehiclecomponent. If the processor 204 determines that the data storage 210contains the different cluster, the processor 204 may then add theparticular RO to this different cluster as discussed above. Whereas, ifthe processor 204 determines that the data storage 210 does not containthe different cluster, the processor 204 may generate this differentcluster and may store this generated different cluster in the datastorage 210. Once the processor 204 generated this different cluster,the processor 204 may then add the particular RO to this differentcluster as discussed above.

The processor 204 may generate the different cluster in one of variousways. For instance, the processor 204 may generate a cluster ID for acombination of vehicle attributes (e.g., meta-data of the attributes)that define the cluster. Such attributes may include particular DTC(s),particular symptom(s), particular component(s), and/or particular laboroperation(s) (e.g., particular LOC(s)), among others. By way of example,the processor 204 may assign a particular cluster ID to a combination of“P0101” and “Mass Airflow Sensor” attributes and may thus associate ROscontaining data representative of such attributes with this particularcluster ID. In this manner, different combinations of vehicle attributesmay be assigned different cluster IDs. Other examples are also possible.

IV. Additional Aspects

In an example implementation, the processor 204 may carry out thevarious sets of operations discussed above but may still not be able todetermine the specific vehicle component. This may be due to theprocessor 204 determining that the particular RO does not includecertain information needed to carry out a certain set of operations, ormay be due to other reasons. Regardless, in such situations theprocessor 204 may generate a report for human review. The purpose of thereport is to provide valuable information that could help to manuallydetermine the specific vehicle component and to then manually categorizethe particular RO into an appropriate cluster. Hence, the report couldindicate that the processor 204 was unable to determine the specificvehicle component and that thus ambiguity still exists with regards tothe non-specific vehicle component.

Additionally or alternatively, the report could provide otherinformation that could help appropriately categorize the particular RO.For example, the processor 204 could identify one or more other ROswithin ROs 214 that have similarities to the particular RO at issue.These similarities may involve the other ROs having data indicating thesame (or similar) vehicle symptoms, the same (or similar) correctiveactions, and/or the same (or similar) non-specific vehicle component.Upon identifying such ROs, the processor 204 could generate the reportto list such ROs and/or perhaps to list valuable information included inthese ROs.

To illustrate, refer again to FIG. 1 showing that the VRD system 102 mayoutput a report 124. In particular, this report 124 could be displayedas part of a graphical user interface (GUI) on a display of the VRDsystem. In another case, the VRD system 102 could transmit the report124 to one or more of the VRTs 106-120 (and/or to other devices) suchthat a VRT could display this report 124. For instance, VRT 300 of FIG.3 is shown to display a report 314 (e.g., could be the same as report124) as part of the user interface 314. In yet another case, the VRDsystem 102 may store the report 124 in data storage 210 and/orcloud-based data storage so as to make the report 124 accessible forhuman review via a device (e.g., a VRT). Other cases are also possible.

V. Conclusion

Example embodiments have been described above. Those skilled in the artwill understand that changes and modifications can be made to thedescribed embodiments without departing from the true scope of thepresent invention, which is defined by the claims.

Additional embodiments, based on the features or functions describedherein, can be embodied as a computer-readable medium storing programinstructions, that when executed by a processor of a machine cause a setof functions to be performed, the set of functions comprising thefeatures or functions of the aspects and embodiments described herein.

We claim:
 1. A method comprising: storing, within a non-transitorycomputer-readable memory, multiple computer-readable vehicle repairorders (ROs), a clusters database, a taxonomy terms database, a partsnumber database, and first meta-data that associates a particularcomputer-readable vehicle repair order (RO) with an existing clusteridentifier, wherein: the multiple ROs include the particular RO, theclusters database includes multiple clusters, each cluster of themultiple clusters corresponds to a respective cluster identifier and arespective set of RO attributes, a cluster of the multiple clusterscontains the particular RO when the particular RO is associated with therespective cluster identifier of the cluster, the multiple clustersinclude an existing cluster of ROs and a different cluster of ROs, theexisting cluster of ROs corresponds to the existing cluster identifierand an existing set of RO attributes, the existing set of RO attributesincludes a particular vehicle symptom, the different cluster of ROscorresponds to a different cluster identifier and a different set of ROattributes, the different set of RO attributes specify the particularvehicle symptom and a specific vehicle component, the particular RO isassociated with the existing cluster identifier and specifies theparticular vehicle symptom, the taxonomy terms database contains datathat identifies a plurality of first taxonomy terms each indicative of arespective vehicle component, and the parts number database containsdata that identifies a mapping of specific vehicle part numbers toparticular part number descriptions, based on the first meta-data thatassociates the particular RO with the existing cluster identifier,determining, by one or more processors, that the particular RO iscontained in the existing cluster of ROs; making a first determination,by the one or more processors, that the particular RO includes, within afirst portion of the particular RO, a first text string representativeof a non-specific vehicle component by determining that the first textstring partially matches two or more taxonomy terms of the plurality offirst taxonomy terms; in response to making the first determination, theone or more processors making a second determination that the particularRO includes, within a second portion of the particular RO, a second textstring indicative of a particular part number; based on the seconddetermination, the one or more processors making a third determinationthat includes inferring the non-specific vehicle component representedby the first text string is the specific vehicle component specified bythe different set of RO attributes by: (i) determining, within the partsnumber database, a first particular part number descriptioncorresponding to the particular part number, and (ii) determining thefirst text string partially matches a portion of the first particularpart number description, and in response to making the thirddetermination, moving the particular RO from the existing cluster of ROsto the different cluster of ROs, wherein moving the particular RO iscarried out by: (i) revising the first meta-data so that the firstmeta-data associates the particular RO with the different clusteridentifier and no longer associates the particular RO with the existingcluster identifier, or (ii) generating second meta-data that associatesthe particular RO with the different cluster identifier and revising thefirst meta-data so that the first meta-data no longer associates theparticular RO with the existing cluster identifier.
 2. The method ofclaim 1, wherein: the particular RO comprises a parts line and aremaining portion of the particular RO, and the first portion of theparticular RO and the second portion of the particular RO are containedon the parts line.
 3. The method of claim 2, wherein the parts lineincludes data indicative of a price corresponding to the non-specificvehicle component represented by the first text string.
 4. The method ofclaim 1, wherein determining the first text string partially matches aportion of the first particular part number description includesdetermining the first text string includes a word that is identical to aword within the first particular part number description.
 5. The methodof claim 1, wherein the particular part number includes one or morenumerals and one or more alphabet letters.
 6. The method of claim 1,wherein the particular vehicle symptom includes a particular diagnostictrouble code (DTC) identifier.
 7. The method of claim 1, wherein: thenon-transitory computer-readable memory has stored thereon an indicationthat the particular RO is associated with the existing clusteridentifier, and moving the particular RO from the existing cluster ofROs to the different cluster of ROs comprises: removing, from thenon-transitory computer-readable memory, the indication that theparticular RO is associated with the existing cluster identifier; andstoring, at the non-transitory computer-readable memory, an indicationthat the particular RO is associated with a different clusteridentifier, and the different cluster identifier is stored within thenon-transitory computer-readable memory to identify the differentcluster of ROs.
 8. The method of claim 1, wherein: the different clusterof ROs contains a particular quantity of ROs after the particular RO ismoved to the different cluster of ROs, and the method further comprises:receiving, by the one or more processors via a vehicle repair tool, arequest that comprises at least one search term specifying at least theparticular vehicle symptom; making a further determination, by the oneor more processors, that the different cluster of ROs is associated withthe at least one search term and that the particular quantity of ROscontained in the different cluster of ROs exceeds a threshold quantity;and based on the further determination, the one or more processorsresponding to the request by causing a display device of the vehiclerepair tool to display a repair-hint associated with the differentcluster of ROs, and the repair-hint specifies at least that a particularrepair of the specific vehicle component resolves the particular vehiclesymptom.
 9. The method of claim 1, wherein the one or more processorsdetermines content of the particular RO for storing within thenon-transitory computer-readable memory by executing an opticalcharacter recognition application while scanning the particular RO. 10.The method of claim 1, wherein: inferring the non-specific vehiclecomponent represented by the first text string is the specific vehiclecomponent specified by the different set of RO attributes furtherincludes statistically inferring the specific vehicle component, andstatistically inferring the specific vehicle component includesdetermining, from among the multiple ROs, a set of ROs that include textindicative of the particular vehicle symptom and that a thresholdpercentage of the set of ROs include text indicative of the non-specificvehicle component.
 11. A computing system comprising: a non-transitorycomputer-readable memory containing multiple computer-readable vehiclerepair orders (ROs), a clusters database, a taxonomy terms database, aparts number database, and first meta-data that associates a particularcomputer-readable vehicle repair order (RO) with an existing clusteridentifier, wherein: the multiple ROs include the particular RO, theclusters database includes multiple clusters, each cluster of themultiple clusters corresponds to a respective cluster identifier and arespective set of RO attributes, a cluster of the multiple clusterscontains the particular RO when the particular RO is associated with therespective cluster identifier of the cluster, the multiple clustersinclude an existing cluster of ROs and a different cluster of ROs, theexisting cluster of ROs corresponds to the existing cluster identifierand an existing set of RO attributes, the existing set of RO attributesincludes a particular vehicle symptom, the different cluster of ROscorresponds to a different cluster identifier and a different set of ROattributes, the different set of RO attributes specify the particularvehicle symptom and a specific vehicle component, the particular RO isassociated with the existing cluster identifier and specifies theparticular vehicle symptom, and the taxonomy terms database containsdata that identifies a plurality of first taxonomy terms each indicativeof a respective vehicle component, and the parts number databasecontains data that identifies a mapping of specific vehicle part numbersto particular part number descriptions, one or more processors coupledto the non-transitory computer-readable memory and programmed to: basedon the first meta-data that associates the particular RO with theexisting cluster identifier, determine that the particular RO iscontained in the existing cluster of ROs, make a first determinationthat the particular RO includes, within a first portion of theparticular RO, a first text string representative of a non-specificvehicle component by determining that the first text string partiallymatches two or more taxonomy terms of the plurality of first taxonomyterms; in response to making the first determination, make a seconddetermination that the particular RO includes, within a second portionof the particular RO, a second text string indicative of a particularpart number; based on the second determination, make a thirddetermination that includes inferring the non-specific vehicle componentrepresented by the first text string is the specific vehicle componentspecified by the different set of RO attributes by: (i) determining,within the parts number database, a first particular part numberdescription corresponding to the particular part number, and (ii)determining the first text string partially matches a portion of thefirst particular part number description, and in response to making thethird determination, move the particular RO from the existing cluster ofROs to the different cluster of ROs, wherein moving the particular RO iscarried out by: (i) revising the first meta-data so that the firstmeta-data associates the particular RO with the different clusteridentifier and no longer associates the particular RO with the existingcluster identifier, or (ii) generating second meta-data that associatesthe particular RO with the different cluster identifier and revising thefirst meta-data so that the first meta-data no longer associates theparticular RO with the existing cluster identifier.
 12. The computingsystem of claim 11, wherein: the particular RO comprises a parts lineand a remaining portion of the particular RO, and the first portion ofthe particular RO and the second portion of the particular RO arecontained on the parts line.
 13. The computing system of claim 12,wherein the parts line includes data indicative of a price correspondingto the non-specific vehicle component represented by the first textstring.
 14. The computing system of claim 11, wherein determining thefirst text string partially matches a portion of the first particularpart number description includes determining the first text stringincludes a word that is identical to a word within the first particularpart number description.
 15. The computing system of claim 11, whereinthe particular part number includes one or more numerals and one or morealphabet letters.
 16. The computing system of claim 11, wherein theparticular vehicle symptom includes a particular diagnostic trouble code(DTC) identifier.
 17. The computing system of claim 11, wherein: thenon-transitory computer-readable memory has stored thereon an indicationthat the particular RO is associated with the existing clusteridentifier, and the one or more processors being programmed to move theparticular RO from the existing cluster of ROs to the different clusterof ROs comprises the one or more processors being programmed to: removethe indication that the particular RO is associated with the existingcluster identifier; and store an indication that the particular RO isassociated with a different cluster identifier, wherein the differentcluster identifier is stored within the non-transitory computer-readablememory to identify the different cluster of ROs.
 18. The computingsystem of claim 11, wherein: the different cluster of ROs contains aparticular quantity of ROs after the particular RO is moved to thedifferent cluster of ROs, and the one or more processors are furtherprogrammed to: receive, via a vehicle repair tool, a request thatcomprises at least one search term specifying at least the particularvehicle symptom; make a further determination that the different clusterof ROs is associated with the at least one search term and that theparticular quantity of ROs contained in the different cluster of ROsexceeds a threshold quantity; and based on the further determination,respond to the request by causing a display device of the vehicle repairtool to display a repair-hint associated with the different cluster ofROs, wherein the repair-hint specifies at least that a particular repairof the specific vehicle component resolves the particular vehiclesymptom.
 19. The computing system of claim 11, wherein: inferring thenon-specific vehicle component represented by the first text string isthe specific vehicle component specified by the different set of ROattributes further includes statistically inferring the specific vehiclecomponent, and statistically inferring the specific vehicle componentincludes determining, from among the multiple ROs, a set of ROs thatinclude text indicative of the particular vehicle symptom and that athreshold percentage of the set of ROs include text indicative of thenon-specific vehicle component
 20. A non-transitory computer-readablememory containing multiple computer-readable vehicle repair orders(ROs), a clusters database, a taxonomy terms database, a parts numberdatabase, and first meta-data that associates a particularcomputer-readable vehicle repair order (RO) with an existing clusteridentifier, wherein: the multiple ROs include the particular RO, theclusters database includes multiple clusters, each cluster of themultiple clusters corresponds to a respective cluster identifier and arespective set of RO attributes, a cluster of the multiple clusterscontains the particular RO when the particular RO is associated with therespective cluster identifier of the cluster, the multiple clustersinclude an existing cluster of ROs and a different cluster of ROs, theexisting cluster of ROs corresponds to the existing cluster identifierand an existing set of RO attributes, the existing set of RO attributesincludes a particular vehicle symptom, the different cluster of ROscorresponds to a different cluster identifier and a different set of ROattributes, the different set of RO attributes specify the particularvehicle symptom and a specific vehicle component, the particular RO isassociated with the existing cluster identifier and specifies theparticular vehicle symptom, and the taxonomy terms database containsdata that identifies a plurality of first taxonomy terms each indicativeof a respective vehicle component, and the parts number databasecontains data that identifies a mapping of specific vehicle part numbersto particular part number descriptions, the non-transitorycomputer-readable memory further contains instructions executable by oneor more processors to cause a computing system to perform functionscomprising: based on the first meta-data that associates the particularRO with the existing cluster identifier, determining that the particularRO is contained in the existing cluster of ROs; making a firstdetermination that the particular RO includes, within a first portion ofthe particular RO, a first text string representative of a non-specificvehicle component by determining that the first text string partiallymatches two or more taxonomy terms of the plurality of first taxonomyterms; in response to making the first determination, making a seconddetermination that the particular RO includes, within a second portionof the particular RO, a second text string indicative of a particularpart number; based on the second determination, making a thirddetermination that includes inferring the non-specific vehicle componentrepresented by the first text string is the specific vehicle componentspecified by the different set of RO attributes by: (i) determining,within the parts number database, a first particular part numberdescription corresponding to the particular part number, and (ii)determining the first text string partially matches a portion of thefirst particular part number description, and in response to making thethird determination, moving the particular RO from the existing clusterof ROs to the different cluster of ROs, wherein moving the particular ROis carried out by: (i) revising the first meta-data so that the firstmeta-data associates the particular RO with the different clusteridentifier and no longer associates the particular RO with the existingcluster identifier, or (ii) generating second meta-data that associatesthe particular RO with the different cluster identifier and revising thefirst meta-data so that the first meta-data no longer associates theparticular RO with the existing cluster identifier.